Transient Fault Area Location and Fault Classification for Distribution

VOLUME: 16 | NUMBER: 2 | 2018 | JUNE

POWER ENGINEERING AND ELECTRICAL ENGINEERING

Transient Fault Area Location and Fault Classification for Distribution Systems Based on Wavelet Transform and Adaptive Neuro-Fuzzy Inference System (ANFIS) Ali KHALEGHI 1 , Mahmoud OUKATI SADEGH 1 , Mahdi GHAZIZADEH-AHSAEE 2 , Alireza MEHDIPOUR RABORI 3 1

Department of Electrical and Electronics Engineering, Faculty of Electrical and Computer Engineering, University of Sistan and Baluchestan, Zahedan, Daneshgah Boulevard, Iran 2 Department of Electrical Engineering, Faculty of Engineering, University of Zabol, Zabol, Iran 3 Department of Electrical Engineering and Computer, Faculty of Engineering, Shahid Bahonar University, Pajoohesh Square, Kerman, Iran [email protected], [email protected], [email protected], [email protected] DOI: 10.15598/aeee.v16i2.2563

Abstract. A novel method to locate the zone of transient faults and to classify the fault type in Power Distribution Systems using wavelet transforms and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) has been developed. It draws on advanced techniques of signal processing based on wavelet transforms, using data sampled from the main feeder current to extract important characteristics and dynamic features of the fault signal. In this method, algorithms designed for fault detection and classification based on features extracted from wavelet transforms were implemented. One of four different algorithms based on ANFIS, according to the type of fault, was then used to locate the fault zone. Studies and simulations in an EMTP-RV environment for the 25 kV power distribution system of Canada were carried out by considering ten types of faults with different fault inception, fault resistance and fault locations. The simulation results showed high accuracy in classifying the type of fault and determining the fault area, so that the maximum observed error was less than 2 %.

Keywords Adaptive Neuro-Fuzzy Inference System (ANFIS), electrical distribution systems, fault classification, fault detection, fault location, wavelet transforms.

1.

Introduction

Fault location is a key issue in the protection of power systems and accurate and swift fault location processes reduce expected energy that will not be supplied, increase system efficiency and promote customer satisfaction with the power distribution system. Implementation of fault location algorithms in power systems needs to consider both transmission and distribution networks. Prolonged fault correction processes in power systems may cause irreparable damage, and consequently, rapid fault detection and correction in these systems is of the utmost importance. Measurements of voltage, current, power and frequency in transmission lines can be made with high precision, allowing the exact fault location to be determined quickly and timely action taken to resolve the problem. A variety of algorithms has been presented in the literature and a number of them have been applied to practical networks [1], [2] and [3]. In distribution networks, each feeder of a distribution substation covers a large area and, unlike transmission networks, it is not a straight line but rather a line composed of several laterals. In addition, each feeder includes a variety of distribution transformers. Therefore, fault location in distribution networks is more challenging, costly, and less accurate than for transmission systems. Few studies have explored this issue [4], [5], [6] and [7].

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Fault location in a distribution network is faced with faulted zone, faulted line and fault point are located in the following problems that complicate the process of turn. Reference [13] proposed a multi-objective optidetermining the fault location: mization method using a Non-Dominated Sorting Genetic Algorithm (NSGA) algorithm to determine the • The wide expansion of distribution network feed- location of faults in the distribution system. ers and their laterals. When a fault occurs in power systems, fast and ac• Different types of overhead and underground cables, with varying cross-sectional areas and phase configurations, in different parts of the distribution network. • The presence of distribution transformers in different parts of the distribution network, with varied nominal capacities and loading factors. • The existence of just one data logger for fault voltage and current at the beginning of the distribution network feeders. The methods for fault location in power systems are divided into two major categories: impedance and travelling waves [5] and [7]. However, as fault location is more difficult in distribution networks, given problems such as several laterals, fault location in distribution networks is divided into two major parts: (1) locating the fault zone, (2) determining the exact location of fault. First, the faulted zone is exited from the network and then exact fault location is determined. The purpose of this paper is to introduce new methods to determine the area of the fault (faulted zone). Reference [8] estimated the fault zone using current patterns, and re-closer-fuse coordination. Reference [9] used an algorithm based on a matrix to locate the fault zone. In this method, the arrays were made of binary data (0 and 1) transmitted from Feeder Terminal Units (FTU). Reference [10] proposed a synchronized voltage-based non-iterative method by taking advantage of the substitution theorem. By replacing the faulted line with a suitably adjusted current source injecting the same amount of transmission line current, an equivalent network was established. A two-stage fault location algorithm using Radial Basis Function (RBF) based Support Vector Machine (SVM) and Scaled Conjugate Gradient (SCALCG)-based Artificial Neural Network (ANN) was proposed in [11]. In the first stage, the magnitudes of the fundamental harmonics of the positive sequence voltage and current signals of the faulted phases were input to RBF-based SVM to get an approximate fault area. In the second stage, the SCALCG-based ANN was implemented to indicate the precise fault location using high frequency characteristics. The impedance-based method proposed in [12], locates the fault in a hierarchical manner, in which the

curate fault classification (for post-fault analysis) and restoration of the system to its original state are of the utmost importance. In many fault location methods, information about the type of fault is the basis of fault location, so the correct classification of faults affects the precise detection of the fault zone. Given the importance of fault classification for relay performance, many studies have focused on fault classification problems in the transmission system [14], [15] and [16]. Studies of fault classification in power systems are divided into two groups: (1) designs that utilize steady state electrical components [17], [18] and [19], and (2) designs that utilize transient electrical components [20], [21] and [22]. For example, Ref. [18] used an algorithm based on fuzzy logic to determine the type of fault in radial unbalanced systems. Reference [20] used a new approach, based on wavelet transforms, to identify and classify the type of fault by comparing the waveforms. Reference [22] developed a new method to classify the type of fault, using Adaptive Neuro-Fuzzy Inference Systems (ANFIS). This method was based on applying wavelet transforms to the fault current. Since most faults occurring in power systems are transient in nature [23], in this paper, we propose a new algorithm to determine the area of transient fault in distribution networks using ANFIS. Based on features extracted from the main feeder current, novel algorithms for detecting and classifying different types of faults are presented. This information is then used to detect the fault zone. Four algorithms were designed to detect the fault zone, one for each type of fault (single-phaseto-ground, double-phase-to-ground, phase-to-phase, three-phase/three-phase-to-ground). The fault zone was then determined using trained ANFIS networks. EMTP-RV is used for simulations. Simulations were carried out in three steps: (1) identification of the fault, (2) classification of the fault type, and (3) location of the faulted zone. This method is less complex than previously reported methods as there are no long and complex calculations involved, and the faulted zone is determined promptly (approximately 4 cycles). This method also has higher accuracy, when compared with other studies [22] and [24]. A further advantage of this method is that identification of the fault and classification of the fault type are completely independent of line and fault parameters.


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2.

Wavelet Transform Analysis

The inputs for the designed algorithms were features extracted from the main feeder current, which were derived from wavelet transforms. Wavelet transforms can be considered as an extension of Fourier transforms, but instead of working on one scale (frequency or time), they work on multiple scales. This multi-scale feature of wavelet transforms leads to the decomposition of a signal into several scales, with each scale representing a particular feature of the signal under study [25]. Wavelet transforms divide the signal into different levels, each level containing frequency-time information for the signal. In this study, features of the profile of transients are taken for the 2000–4000 Hz range. The process for Multi-Resolution Analysis (MRA) of the input signal is shown in Fig. 1. Input signal 2000-4000 Hz

0-2000 Hz

0-1000 Hz

0-125 Hz LPF 0-62.5 Hz LPF

LPF

62.5-125 Hz

LPF

250-500 Hz

LPF

HPF

500-1000 Hz

0-500 Hz

0-250 Hz

1000-2000 Hz LPF

125-250 Hz

HPF

HPF

HPF

HPF

HPF

0-62.5 Hz

62.5-500 Hz

Main frequency Characteristics

Harmonic Characteristics

500-4000 Hz

Transient Characteristics

Fig. 1: Frequency division multi-resolution levels up to 6.

3.

4.

Estimating Fault Time Algorithm

Firstly, the main feeder currents in each cycle are received and their essential characteristics are obtained from their wavelet transforms. Based on a waveform analysis of the fault signal at different times, it was found that in all cases, the waveform obtained from the wavelet transform of the main feeder current, at the time of transient fault, possessed the highest jump. By comparing momentary variations in a sample with the previous sample in each cycle, the maximum variation of the wavelet transform can be detected. If there were no fault in the selected cycle, the sum of variations would be equal to zero. If a fault takes place, the time of maximum change is considered as the time of fault occurrence. For example, wavelet transform of phase-A during fault occurrence in node 6 of the 25 kV power distribution system of Canada [27] with a resistance of 40 ohms and a fault inception of 10 degrees, is shown in Fig. 2. Figure 3 shows changes from moment to moment. It can be seen that the moment with the highest change in value was considered as the fault occurrence time. As shown in the flowchart in Fig. 4, to avoid interference in detecting the time of the transient fault with enter or exit loads, after determining the start time of disturbance in the wavelet transform current signal, the duration of the disturbance is calculated using a transient detection flag. If this time is less than 3 cycles, the disturbance in the current signal is considered as a transient fault and the fault time is estimated. Using the algorithm in Fig. 4, the fault detection time and fault occurrence were 4 microseconds, thereby indicating high precision in a cycle of 16.6 milliseconds.

Adaptive Neuro-Fuzzy Inference Systems (ANFIS)

ANFIS is one of the models of neuro-fuzzy systems. Neural networks and fuzzy systems are both indepenDisturbance duration dent systems. Increasing training processes, increasing membership functions, and independent fuzzy rules are factors in complexizing problem solving. This led to the development of the ANFIS method, which combines the benefits of both neural networks and fuzzy logic. ANFIS aims to eliminate the disadvantages of each of these systems while retaining their complementary benefits [26]. Fuzzy logic in this system is used as a contributor to the training algorithm and can adjust Fig. 2: Current wavelet transform of phase A (During fault). the parameters of the fuzzy system.


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based on features extracted from the main feeder current is presented. Analysis of the wavelet of the fault signals revealed that signals extracted from wavelet transforms displayed specific behavior for each type of fault. For example, in the case of phase-to-phase faults, the sum of the wavelet transforms of the phases involved from the main feeder current was almost equal to zero (Fig. 5), while in the case of single-phase-toground faults, wavelet transforms of the two phases without faults were almost equal. Figure 6 shows the wavelet transforms of the three-phases when a C-phaseto-ground fault took place. It can be seen that the Phase-C wavelet transform with the fault is distinct, but wavelet transforms for the other two phases display similar behavior.

Disturbance duration

Fig. 3: Instantaneous changes of the current wavelet transform of phase A.

Receive current of main feeder Ia,Ib,Ic

Next cycle

Wavelet Transform SWa=WT(Ia) SWb=WT(Ib) SWc=WT(Ic)

ΔSWa= SWa(i)-SWa(i-1) ΔSWb= SWb(i)-SWb(i-1) ΔSWc= SWc(i)-SWc(i-1)

Yes

(ΔSWa) 0 and (ΔSWb) 0 and (ΔSWc) 0 No

What time is maximum of ΔSWa? or What time is maximum of ΔSWb? or What time is maximum of ΔSWc?

Transient Fault has not occurred

Maximum of (ΔSWa, ΔSWb, ΔSWc) is Considered as Time of fault

Calculate Disturbance Duration using Transient Detection flag

Maximum of (ΔSWa, ΔSWb, ΔSWc) is Time Transient fault occurred

Fig. 5: Wavelet transform of three-phase current during AC phase-to-phase fault (during fault).

>3 cycle